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Automated image quality evaluation of T 2 ‐weighted liver MRI utilizing deep learning architecture
Author(s) -
Esses Steven J.,
Lu Xiaoguang,
Zhao Tiejun,
Shanbhogue Krishna,
Dane Bari,
Bruno Mary,
Chandarana Hersh
Publication year - 2018
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.25779
Subject(s) - convolutional neural network , computer science , artificial intelligence , concordance , magnetic resonance imaging , image quality , deep learning , image (mathematics) , t2 weighted , pattern recognition (psychology) , predictive value , algorithm , nuclear medicine , radiology , medicine
Purpose To develop and test a deep learning approach named Convolutional Neural Network (CNN) for automated screening of T 2 ‐weighted (T 2 WI) liver acquisitions for nondiagnostic images, and compare this automated approach to evaluation by two radiologists. Materials and Methods We evaluated 522 liver magnetic resonance imaging (MRI) exams performed at 1.5T and 3T at our institution between November 2014 and May 2016 for CNN training and validation. The CNN consisted of an input layer, convolutional layer, fully connected layer, and output layer. 351 T 2 WI were anonymized for training. Each case was annotated with a label of being diagnostic or nondiagnostic for detecting lesions and assessing liver morphology. Another independently collected 171 cases were sequestered for a blind test. These 171 T 2 WI were assessed independently by two radiologists and annotated as being diagnostic or nondiagnostic. These 171 T 2 WI were presented to the CNN algorithm and image quality (IQ) output of the algorithm was compared to that of two radiologists. Results There was concordance in IQ label between Reader 1 and CNN in 79% of cases and between Reader 2 and CNN in 73%. The sensitivity and the specificity of the CNN algorithm in identifying nondiagnostic IQ was 67% and 81% with respect to Reader 1 and 47% and 80% with respect to Reader 2. The negative predictive value of the algorithm for identifying nondiagnostic IQ was 94% and 86% (relative to Readers 1 and 2). Conclusion We demonstrate a CNN algorithm that yields a high negative predictive value when screening for nondiagnostic T 2 WI of the liver. Level of Evidence : 2 Technical Efficacy : Stage 2 J. Magn. Reson. Imaging 2018;47:723–728.